In this work-flow we would like to examine 4 different protoclos for astrocytes isolation.
Loading the library:
library("metacell")
To start using MetaCell, you first initialize a database. This is not much more than linking the package to directory that stores all your objects. In our case we will initialize the database to the saved_work directory:
wdir = "/home/labs/amit/diklag/thesis/astro/no_spinal_cord_new/"
setwd(wdir)
if(!dir.exists("saved_work")) dir.create("saved_work/")
scdb_init("saved_work/", force_reinit=T)
## initializing scdb to saved_work/
tgconfig::override_params("annotations/astro_params.yaml","metacell")
src_functions = "../sc_aux_functions.R"
force_reinit=T instruct the system to override existing database objects. This can be important if you are running in cycles and would like to update your objects. Otherwise, the database reuses loaded objects to save time on reading and initializing them from the disk.
tgconfig::override_params overrides default metacell configurations
Before starting to analyze the data, we link the package to a figure directory:
if(!dir.exists("results")) dir.create("results/")
scfigs_init("results/")
We will read multiple SPID umi matrices (umi.tab) and merge them, based on a table defining the datasets @param mat_nm defines the ID of the matrix object (and is going to be the name of all the objects from now on) @param base_dir defines the umitab directory @param mat_nm defines the name (id) of matrix @param datasets_table_fn defines the index file of the SPID multi batch dataset. This is a tab delimited text file, with an arbitrary number of columns and a header line. The three mandatory fields are: Amp.Batch.ID - specify the ID of the batch defined by the row, and also the file name (without the .txt suffix) of the respective umi table in the base_dir provided. Seq.Batch.ID - efines and ID of the sequencing batch (may be relevant for further noise cleanups beyond those done in the low-level pipeline). Batch.Set.ID - The third id group different batches into sets for downstream analysis (e.g. QC and more).
index_fn = "annotations/astro.txt"
id = "brain"
Let us take a look at our index file:
| Amp.Batch.ID | Seq.Batch.ID | Batch.Set.ID | Protocol_version_ID | Pool_barcode | R2_design | Experiment_ID | Owner | Species | Empty_wells | ng.ul | length | QC.Primer | QC1..pooled.cDNA. | QC2..FinalLib. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AB952 | SB57 | Quintana protocol 1 brain P1 | SPID V0.9 | ATTGCGAC | 7W.8R | Quintana protocol 1 brain | Mor_K | Mouse | O1,O2,P1,P2 | 8.06 | 358 | mActb | 19.705 | 11.470 |
| AB953 | SB57 | Quintana protocol 1 brain P2 | SPID V0.9 | GTCTCATC | 7W.8R | Quintana protocol 1 brain | Mor_K | Mouse | O1,O2,P1,P2 | 12.00 | 400 | mActb | 19.865 | 10.740 |
| AB954 | SB57 | Quintana protocol 2 brain P1 | SPID V0.9 | GCGTGACC | 7W.8R | Quintana protocol 2 brain | Mor_K | Mouse | O1,O2,P1,P2 | 11.00 | 421 | mActb | 19.330 | 10.815 |
| AB955 | SB57 | Quintana protocol 2 brain P2 | SPID V0.9 | ATCGGATC | 7W.8R | Quintana protocol 2 brain | Mor_K | Mouse | O1,O2,P1,P2 | 10.10 | 405 | mActb | 19.520 | 10.860 |
| AB956 | SB57 | Quintana protocol 3 brain P1 | SPID V0.9 | CTTGCACC | 7W.8R | Quintana protocol 3 brain | Mor_K | Mouse | O1,O2,P1,P2 | 11.90 | 399 | mActb | 18.990 | 10.535 |
| AB957 | SB57 | Quintana protocol 3 brain P2 | SPID V0.9 | ACTGGAAC | 7W.8R | Quintana protocol 3 brain | Mor_K | Mouse | O1,O2,P1,P2 | 7.27 | 327 | mActb | 18.780 | 10.335 |
Let’s load a matrix to the system:
umi.tab_dir = "/home/labs/amit/eyald/sc_pipeline/scdb_v4_mouse/output/umi.tab/"
mcell_import_multi_mars(mat_nm = id, dataset_table_fn = index_fn, base_dir = umi.tab_dir, force = T)
## will read AB952
## will read AB953
## will read AB954
## will read AB955
## will read AB956
## will read AB957
## will read AB958
## will read AB959
## [1] TRUE
mat = scdb_mat(id)
print(dim(mat@mat))
## [1] 52634 3072
The scdb_mat() command returns a matrix object, which has one slot containing the count matrix - mat@mat, as well as additional features we will mention below.
MetaCell uses a standardized naming scheme for the figures, to make it easier to archive and link analysis figures to the database objects. In principle, figures in the figures directory are named after the object data type they refer to (for example, mat for matrices, mc for metacells, and more, see below). The figure name then includes also the object name they refer to, and a suffix describing the actual figure type.
To get a basic understanding of the new data, we will plot the distribution of UMI count per cell (the plot is thresholded after 50 bins):
mcell_plot_umis_per_cell(id,min_umis_cutoff = 100)
## [1] 100
Umi distribution plot
We want to clean some known issues from the matrix before starting to work with it. We generate a list of mitochondrial genes that typically mark cells as being stressed or dying, as well as immunoglobulin genes that may represent strong clonal signatures in plasma cells, rather than cellular identity.
mat = scdb_mat(id)
nms = c(rownames(mat@mat), rownames(mat@ignore_gmat))
bad_genes = c(grep("ERCC", nms, v=T),
grep("^Gm[0-9]", nms, v=T),
grep(".*Rik$",nms,v=T),
grep("^mt-", nms, v=T),
grep("Malat1", nms, v=T))
We will next ask the package to ignore the above genes and ignore erithrocytes:
mcell_mat_ignore_genes(new_mat_id=id, mat_id=id, bad_genes, reverse=F)
Ignored genes are kept in the matrix for reference, but all downstream analysis will disregard them. This means that the number of UMIs from these genes cannot be used to distinguish between cells.
In the current example we will also eliminate cells with less than 500 UMIs (threshold can be set based on examination of the UMI count distribution):
mcell_mat_ignore_small_cells(id, id, 100)
Note that filtering decisions can be iteratively modified given results of the downstream analysis.
We move on to computing statistics on the distributions of each gene in the data, which are going to be our main tool for selecting feature genes for MetaCell analysis:
mcell_add_gene_stat(gstat_id=id, mat_id=id, force=T)
## Calculating gene statistics...
## will downsamp
## done downsamp
## will gen mat_n
## done gen mat_n
## done computing basic gstat, will compute trends
## ..done
This generates a new object of type gstat under the name ‘r id’, by analyzing the count matrix with id ‘r id’. We can explore interesting genes and their distributions:
gstat = scdb_gstat(id)
t_vm = quantile(gstat$ds_vm_norm,c(1:10)/10)[8]
We can also move directly to select a gene set for downstream analysis: We create a new object of type gset (gene set), to which all genes whose scaled variance (variance divided by mean) exceeds a given threshold are added:
mcell_gset_filter_multi(gstat_id=id, gset_id=id, T_tot=50, T_top3=4, T_vm = t_vm, force_new = T)
## Selected 1026 markers
The command creates a new gene set with all genes for which the scaled variance is higher than 90% of the genes (top decile), it also restricts this gene set to genes with at least 50 UMIs across the entire dataset, and also requires selected genes to have at least three cells for more than 4 UMIs were recorded.
We update the gset object by removing a list of irrelevant genes which we don’t want them to affect the clustering process
gset_unfiltered = scdb_gset(id)
rp_markers = grep("Rpl|Rps|mt-", names(gset_unfiltered@gene_set), v=T)
markers_to_keep = setdiff(names(gset_unfiltered@gene_set), unique(c(bad_genes,rp_markers)))
scdb_del_gset(id)
## [1] TRUE
tmp = rep(1, length(markers_to_keep)); names(tmp) = markers_to_keep
new_gset = gset_new_gset(tmp, "ribos filtered out")
scdb_add_gset(id, new_gset)
We can refine our parameters by plotting all genes and our selected gene set given the mean and variance statistics:
mcell_plot_gstats(gstat_id=id, gset_id=id)
## png
## 2
var mean plot
Assuming we are happy with the selected genes, we will move forward to create a similarity graph (cgraph), using a construction called balanced K-nn graph:
set.seed(27)
mcell_add_cgraph_from_mat_bknn(mat_id=id,gset_id = id,graph_id=id,K=100,dsamp=T)
## will downsample the matrix, N= 750
## will build balanced knn graph on 2974 cells and 1025 genes, this can be a bit heavy for >20,000 cells
This adds to the database a new cgraph object named test_graph. The K=100 parameter is important, as it affects the size distribution of the derived metacells. Note that constructing the graph can become computationally intensive if going beyond 20-30,000 cells. The system is currently limited by memory, and we have generated a graph on 160,000 cells on machines with 0.5TB RAM..
The next step will use the cgraph to sample five hundred metacell partitions, each covering 75% of the cells and organizing them in dense subgraphs:
set.seed(27)
mcell_coclust_from_graph_resamp(coc_id=id,graph_id=id,min_mc_size=15,p_resamp=0.75, n_resamp=500)
## running bootstrap to generate cocluster
## 2%...6%...14%...20%...28%...37%...43%...51%...59%...65%...72%...78%...86%...94%...100%
## done resampling
The metacell size distribution of the resampled partitions will be largely determined by the K parameter used for computing the cgraph. The resampling process may take a while if the graphs are very large. You can modify n_resamp to generate fewer resamples.
The resampling procedure creates a new coclust object in the database named ‘r id’, and stores the number of times each pair of cells ended up being part of the same metacell. The co-clustering statistics are used to generate a new similarity graph, based on which accurate calling of the final set of metacells is done:
set.seed(27)
mcell_mc_from_coclust_balanced(coc_id=id,mat_id= id,mc_id= id,K=30, min_mc_size=15, alpha=2)
## filtered 659392 left with 188948 based on co-cluster imbalance
## building metacell object, #mc 36
## add batch counts
## compute footprints
## compute absolute ps
## compute coverage ps
## reordering metacells by hclust and most variable two markers
## reorder on C1qb vs Dbi
We created a metacell object’r id’ based on analysis of the co-clustering graph. The parameter K determines the number of neighbors we wish to minimally associate with each cell. Prior to partitioning the co-cluster graph is filtered to eliminate highly unbalanced edges, with smaller alpha resulting in harsher filtering.
We now have a preliminary metacell object. It is a good practice to make sure all metacells within it are homogeneous. This is done by the outlier scan procedure, which splits metacells whose underlying similarity structure supports the existence of multiple sub-clusters, and removes outlier cells that strongly deviate from their metacell’s expression profile.
fc = 4
new_id = paste0(id,"_f")
mcell_mc_split_filt(new_mc_id=new_id,
mc_id=id,
mat_id=id,
T_lfc=fc, plot_mats=F)
## starting split outliers
## splitting metacell 26
## splitting metacell 27
## add batch counts
## compute footprints
## compute absolute ps
## compute coverage ps
We will first assign random colors to our clusters (these can later be modified with custom color definitions, e.g. based on cell type assignments).
mc<- scdb_mc(new_id)
mc@colors <- colorRampPalette(c("darkgray", "burlywood1", "chocolate4","orange", "red", "purple", "blue","darkgoldenrod3", "cyan"))(ncol(mc@mc_fp))
no_color_mc = paste0("no_color_",new_id)
scdb_add_mc(id = no_color_mc,mc = mc)
scdb_add_mc(id = new_id,mc = mc)
mc<- scdb_mc(no_color_mc)
The filtered metacell object ‘r id’ can now be visualized. In order to do this effectively, we usually go through one or two iterations of selecting informative marker genes. The package can select markers for you automatically - by simply looking for genes that are strongly enriched in any of the metacells:
mcell_gset_from_mc_markers(gset_id=paste0(new_id,"_markers"), mc_id=no_color_mc)
mcell_mc_plot_marks(mc_id=no_color_mc, gset_id=paste0(new_id,"_markers"), mat_id=id,plot_cells = F)
heatmap_marks_mc
While 2D projections are popular and intuitive (albeit sometimes misleading) ways to visualize scRNA-seq results, we can also summarize the similarity structure among metacells using a “confusion matrix” which encodes the pairwise similarities between all metacells. This matrix may capture hierarchical structures or other complex organizations among metacells.
We first create a hierarchical clustering of metacells, based on the number of similarity relations between their cells:
mc_hc = mcell_mc_hclust_confu(mc_id=no_color_mc,graph_id=id)
Next, we generate clusters of metacells based on this hierarchy, and visualize the confusion matrix and these clusters. The confusion matrix is shown at the bottom, and the top panel encodes the cluster hierarchy (subtrees in blue, sibling subtrees in gray):
mc_sup = mcell_mc_hierarchy(mc_id=no_color_mc,mc_hc=mc_hc, T_gap=0.04)
save(file="saved_work/mc_hc_sup.Rda",mc_hc,mc_sup)
source(src_functions)
local_mcell_mc_plot_hierarchy(mc_id=no_color_mc,graph_id=id,mc_order=mc_hc$order,sup_mc = mc_sup,width=3500, height=3500, min_nmc=2)
## png
## 2
confusion matrix
source(src_functions)
mc = scdb_mc(no_color_mc)
sup_col = data.frame(supid=c(),color=c(),name=c())
sup_col = rbind(sup_col,data.frame(supid=c(6),color=c("red"),name=c("Microglia")))
sup_col = rbind(sup_col,data.frame(supid=c(12),color=c("green"),name=c("Astrocytes")))
sup_col = rbind(sup_col,data.frame(supid=c(13),color=c("purple"),name=c("Oligodendrocytes")))
sup_col = rbind(sup_col,data.frame(supid=c(19),color=c("gray"),name=c("Others")))
sup_col = rbind(sup_col,data.frame(supid=c(14),color=c("brown"),name=c("Epithelial")))
sup_col = rbind(sup_col,data.frame(supid=c(18),color=c("navy"),name=c("Neutrophils")))
sup_col = rbind(sup_col,data.frame(supid=c(20),color=c("pink"),name=c("T cells")))
# 13 yellow granulocytes
write.table(sup_col,file = "annotations/sup_col.txt",row.names = FALSE,sep = "\t")
gene_key = data.frame(name = c(), gene =c(), color = c(), T_fold = c())
gene_key = rbind(gene_key,data.frame(name = c("monocytes"), gene =c("H2-Aa"), color = c("blue"), T_fold = c(6)))
write.table(gene_key, file="annotations/gene_key.txt",row.names = FALSE,sep = "\t")
mc_colorize_sup_hierarchy(mc_id = new_id,supmc = mc_sup,supmc_key = "annotations/sup_col.txt",gene_key = "annotations/gene_key.txt")
local_mcell_mc_plot_hierarchy(mc_id=new_id,graph_id=id,mc_order=mc_hc$order,sup_mc = mc_sup,width=3500, height=3500, min_nmc=2)
## png
## 2
After coloring, our confusion matrix looks like that:
Heat maps are useful but sometimes hard to interprets, and so we may want to visualize the similarity structure among metacells (or among cells within metacells). To this end we construct a 2D projection of the metacells, and use it to plot the metacells and key similarities between them (shown as connecting edges), as well as the cells. This plot will use the same metacell coloring we established before (and in case we improve the coloring based on additional analysis, the plot can be regenerated):
set.seed(27)
source(src_functions)
mcell_mc2d_force_knn(mc2d_id= new_id,mc_id=new_id, graph_id=id)
## comp mc graph using the graph brain and K 20
tgconfig::set_param("mcell_mc2d_height",2200, "metacell")
tgconfig::set_param("mcell_mc2d_width",2200, "metacell")
local_mcell_mc2d_plot(mc2d_id=new_id,plot_edges = T)
## png
## 2
Note that we changed the metacell parameters “mcell_mc2d_height/width” to get a reasonably-sized figure. There are many additional parameters that can be tuned in MetaCell, and more of those meant for routine tuning will be discussed in other vignettes. We obtain the following figure:
mcell_mc_plot_marks(mc_id=new_id, gset_id=paste0(new_id,"_markers"), mat_id=id,plot_cells = F)
heatmap_marks_mc
# generates the excel file of clusters and genes
mcell_mc_export_tab(mc_id=new_id,gstat_id=id,mat_id=id,T_gene_tot=100, metadata_fields=c("amp_batch_id"))
pretty10exp <-function (x, drop.1 = FALSE, digits.fuzz = 7)
{
mi = min(x,na.rm = TRUE)
eT <- floor(log10(abs(x)) + 10^-digits.fuzz)
mT <- signif(x/10^eT, digits.fuzz)
ss <- vector("list", length(x))
for (i in seq(along = x)) ss[[i]] <- if (is.na(x[i]))
quote(NA)
else if (x[i] == 0)
quote(0)
else if (drop.1 && mT[i] == 1)
substitute(10^E, list(E = eT[i]))
else if (drop.1 && x[i] == mi)
mi
else if (drop.1 && mT[i] == -1)
substitute(-10^E, list(E = eT[i]))
#else substitute(A %*% 10^E, list(A = mT[i], E = eT[i]))
else ""
do.call("expression", ss)
}
flow_breaks <- function(x, n = 6, equal.space = FALSE, trans.fun, inverse.fun){
rng.raw <- range(x, na.rm = TRUE)
if(equal.space){
rng <- trans.fun(rng.raw)
min <- floor(rng[1])
max <- ceiling(rng[2])
if (max == min)
return(inverse.fun(min))
by <- (max - min)/(n-1)
myBreaks <- inverse.fun(seq(min, max, by = by))
}else{
#log10 (e.g. 0, 10, 1000, ...)
base10raw <- unlist(lapply(1:n,function(e)10^e))
base10raw <- unlist(lapply(base10raw, function(x) x*c(1:9)))
base10raw <- c(0,base10raw)
base10raw_minus = c()
if(rng.raw[1] < 0){
base10raw_minus <- unlist(lapply(ceiling(log10(abs(rng.raw[1]))):1,function(e)-10^e))
base10raw_minus <- unlist(lapply(base10raw_minus, function(x) x*c(9:1)))
}
myBreaks <- unique(c(base10raw_minus,base10raw[base10raw>rng.raw[1]&base10raw<rng.raw[2]]))
}
myBreaks
}
flow_trans <- function(name, trans.fun, inverse.fun, equal.space = FALSE, n = 6){
brk <- function(x){
flow_breaks(x, n = n, equal.space = equal.space, trans.fun, inverse.fun)
}
if(equal.space){
fmt <- format_format(digits = 0)
}else{
fmt <- function(x){
pretty10exp(as.numeric(x),drop.1=TRUE)
}
}
trans_new(name, transform = trans.fun, inverse = inverse.fun, breaks = brk, format = fmt)
}
logicle_trans <- function(..., n = 6, equal.space = FALSE){
trans.obj <- logicleTransform(...)
trans <- trans.obj@.Data
inv <- inverseLogicleTransform(trans = trans.obj)@.Data
flow_trans(name = "logicle", trans.fun = trans, inverse.fun = inv, n = n, equal.space = equal.space)
}
scale_x_logicle <- function(..., w = 0.5, t = 262144, m = 4.5, a = 0){
myTrans <- logicle_trans(w = w, t = t, m = m, a = a)
scale_x_continuous(..., trans = myTrans)
}
scale_y_logicle <- function(..., w = 0.5, t = 262144, m = 4.5, a = 0){
myTrans <- logicle_trans(w = w, t = t, m = m, a = a)
scale_y_continuous(..., trans = myTrans)
}
In order to run properly the functions for FACS data you need:
@param amp_batches_file for example - “annotations/amp_batch2index_file_name.csv”
@param wells_cells_file for example - “annotations/wells_cells.txt”
You also need to save in a specific place all the FACS csv files (for example: “facs_data/csv_files/”)
amp_batches_file = "annotations/amp_batch2index_file_name.csv"
wells_cells_file = "annotations/wells_cells.txt"
amp_batches = read.csv(amp_batches_file,stringsAsFactors = FALSE)
inx = lapply(amp_batches[1:8,"csv_file"],function(x) read.csv(paste0("facs_data/csv_files/",x),skip = 15,stringsAsFactors = FALSE))
names(inx) = amp_batches[1:8,"Amp.Batch.ID"]
inx = lapply(inx, function(x) {
x[,c(2:ncol(x))] = apply(x[,c(2:ncol(x))],2, function(y) as.double(sub(",","",y)))
return(x)})
well_cells = read.delim(wells_cells_file,stringsAsFactors = FALSE)
well_cells = subset(well_cells,well_cells$Amp_batch_ID %in% amp_batches[,"Amp.Batch.ID"] )
m = lapply(names(inx),function(x) merge(inx[[x]],well_cells[well_cells$Amp_batch_ID == x, c(1,2)],all.x=TRUE,by.x="Well",by.y="well_coordinates"))
names(m) = names(inx)
m = lapply(m, function(x){
rownames(x) = x$Well_ID
x = x[-ncol(x)]
x = x[order(x$All.Events.Time.Mean),]
return(x)
})
library(plyr)
facs = ldply(lapply(m,function(x){
colnames(x) = sub("All.Events.","",colnames(x))
colnames(x) = sub(".Mean","",colnames(x))
colnames(x)[12] = "ACSA-2"
x$Well_ID = rownames(x)
return(x)}),.id = "amp_batch_ID",data.frame)
rownames(facs) = facs$Well_ID
facs = facs[-ncol(facs)]
mc = scdb_mc(new_id)
color2name = mc@color_key$group
names(color2name) = mc@color_key$color
df = data.frame(Well_ID = names(mc@mc),mc=as.integer(mc@mc),color=mc@colors[as.integer(mc@mc)], cell_type = color2name[mc@colors[as.integer(mc@mc)]])
df = merge(df,facs,by.x="Well_ID",by.y=0)
library(flowCore)
library(ggcyto)
library(scales)
##### x and y axes with logicle transformation #####
pairs = list(c("FSC.A","SSC.A"),c("FSC.A","DAPI.Pacific.Blue.A"),c("CD45.APC.A","CD11b.BV605.A"),c("CD45.APC.A","ACSA.2"))
t = 1e6
l = 4.5
m = 4.5
for(pair in pairs){
g = ggplot(df,aes_string(x = pair[1],y = pair[2])) + geom_jitter(size = 2, aes(color = cell_type)) +
scale_color_manual(values = names(color2name) ,breaks= as.character(color2name),limits = as.character(color2name),labels = as.character(color2name)) +
scale_x_logicle(t = 10^ceiling(log10(max(df[[pair[1]]]))),m=m,w = (l-log10(t/abs(min(df[[pair[1]]]))))/2) +
scale_y_logicle(t = 10^ceiling(log10(max(df[[pair[2]]]))),m=m ,w=(l-log10(t/abs(min(df[[pair[2]]]))))/2) +
theme_classic() +
theme(plot.title = element_text(size = 18, face = "bold"),
legend.title =element_blank() ,legend.text=element_text(size = 14,family="Sans"),legend.box.spacing = unit(0.5,"in"),legend.key.width = unit(0.7,"in"),
legend.position = "bottom",legend.box="horizontal", legend.background = element_rect(colour="#E0E0E0", size=.5, linetype="twodash")) +
guides(color = guide_legend(title.position="top", title.hjust = 0.5))
ggsave(paste0("results/",pair[1],"_",pair[2],".png"),width = 12, height = 12)
}
g = ggplot(df,aes_string(x = "CD45.APC.A",y = "ACSA.2")) + geom_jitter(size = 2, aes(color = cell_type)) +
scale_color_manual(values = names(color2name) ,breaks= as.character(color2name),limits = as.character(color2name),labels = as.character(color2name)) +
scale_x_logicle(t = 10^ceiling(log10(max(df[[pair[1]]]))),m=m,w = (l-log10(t/abs(min(df[[pair[1]]]))))/2) +
scale_y_logicle(t = 10^ceiling(log10(max(df[[pair[2]]]))),m=m ,w=(l-log10(t/abs(min(df[[pair[2]]]))))/2) +
theme_classic() + facet_wrap(~amp_batch_ID,nrow = 2) +
theme(plot.title = element_text(size = 18, face = "bold"),
legend.title =element_blank() ,legend.text=element_text(size = 14,family="Sans"),legend.box.spacing = unit(0.5,"in"),legend.key.width = unit(0.7,"in"),
legend.position = "bottom",legend.box="horizontal", legend.background = element_rect(colour="#E0E0E0", size=.5, linetype="twodash")) +
guides(color = guide_legend(title.position="top", title.hjust = 0.5))
ggsave(paste0("results/","CD45.APC.A_ACSA.2_amp_batch.png"),width = 22, height = 12)
We can see that cells assigned to astrocytes have also ACSA.2 records higher than 10^3 and CD45 records lower than 10^2
CD45.APC.A_vs_ACSA.2
CD45.APC.A_vs_ACSA.2_by_Amp_Batch_ID